Abstract
This study proposes particle swarm optimization (PSO) based algorithms to solve multi-objective engineering optimization problems involving continuous, discrete and/or mixed design variables. The original PSO algorithm is modified to include dynamic maximum velocity function and bounce method to enhance the computational efficiency and solution accuracy. The algorithm uses a closest discrete approach (CDA) to solve optimization problems with discrete design variables. A modified game theory (MGT) approach, coupled with the modified PSO, is used to solve multi-objective optimization problems. A dynamic penalty function is used to handle constraints in the optimization problem. The methodologies proposed are illustrated by several engineering applications and the results obtained are compared with those reported in the literature.
| Original language | English |
|---|---|
| Pages (from-to) | 737-752 |
| Number of pages | 16 |
| Journal | Engineering Optimization |
| Volume | 41 |
| Issue number | 8 |
| DOIs | |
| State | Published - Aug 1 2009 |
Fingerprint
Keywords
- Game theory
- Multi-objective optimization
- Particle swarm optimization
ASJC Scopus subject areas
- Control and Optimization
- Industrial and Manufacturing Engineering
- Applied Mathematics
- Computer Science Applications
- Management Science and Operations Research
Cite this
Multi-objective optimization of engineering systems using game theory and particle swarm optimization. / Annamdas, Kiran K.; Rao, Singiresu S.
In: Engineering Optimization, Vol. 41, No. 8, 01.08.2009, p. 737-752.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Multi-objective optimization of engineering systems using game theory and particle swarm optimization
AU - Annamdas, Kiran K.
AU - Rao, Singiresu S
PY - 2009/8/1
Y1 - 2009/8/1
N2 - This study proposes particle swarm optimization (PSO) based algorithms to solve multi-objective engineering optimization problems involving continuous, discrete and/or mixed design variables. The original PSO algorithm is modified to include dynamic maximum velocity function and bounce method to enhance the computational efficiency and solution accuracy. The algorithm uses a closest discrete approach (CDA) to solve optimization problems with discrete design variables. A modified game theory (MGT) approach, coupled with the modified PSO, is used to solve multi-objective optimization problems. A dynamic penalty function is used to handle constraints in the optimization problem. The methodologies proposed are illustrated by several engineering applications and the results obtained are compared with those reported in the literature.
AB - This study proposes particle swarm optimization (PSO) based algorithms to solve multi-objective engineering optimization problems involving continuous, discrete and/or mixed design variables. The original PSO algorithm is modified to include dynamic maximum velocity function and bounce method to enhance the computational efficiency and solution accuracy. The algorithm uses a closest discrete approach (CDA) to solve optimization problems with discrete design variables. A modified game theory (MGT) approach, coupled with the modified PSO, is used to solve multi-objective optimization problems. A dynamic penalty function is used to handle constraints in the optimization problem. The methodologies proposed are illustrated by several engineering applications and the results obtained are compared with those reported in the literature.
KW - Game theory
KW - Multi-objective optimization
KW - Particle swarm optimization
UR - http://www.scopus.com/inward/record.url?scp=70449602576&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70449602576&partnerID=8YFLogxK
U2 - 10.1080/03052150902822141
DO - 10.1080/03052150902822141
M3 - Article
AN - SCOPUS:70449602576
VL - 41
SP - 737
EP - 752
JO - Engineering Optimization
JF - Engineering Optimization
SN - 0305-215X
IS - 8
ER -